Biometric monitoring devices are moving data collection to real-time or near-real-time, but their impact and use in clinical trials is still emerging.
Standardizing real-time data to drive drug development
Always-on biometric monitoring devices allow researchers and physicians to see differences in patterns of biometrics over time, and have already created a huge shift in certain therapeutic areas such as diabetes and cardiac research and care. Connected biometric monitoring devices are becoming increasingly familiar, and many health-conscious people don't leave the house without some sort of activity monitor.
There has been a similar growth in biometric monitoring devices in clinical trials, although the CE-marked and FDA-certified devices used by physicians and clinical research staff are very different from consumer monitors. Medical grade devices generally gather data in greater amounts of detail, using methods that are more tightly defined. For example, consumer actigraphy devices gather accurate data on steps, sleep and heart rate but don’t generally publish the algorithms they use to interpret this raw data. Medical grade devices gather much of the same data, but at a much higher sample rate intended for analysis of gait, muscle activity and other biomarkers, and this creates a much higher volume of data. The devices then analyze these data using algorithms that are published or are well-understood.
The enormous increase in the amount of data collected using biometric devices produces both opportunities and potential pitfalls in clinical trials for both large and emerging biopharma companies. The challenge will be to balance these and create standardized and cost-effective ways to make the most of the data, particularly for small companies and companies in niche indications that are looking to create affordable clinical trials.
The growth in volume and value of data
In the past, monitoring during clinical trials took place during a patient’s visits to a physician’s office or to a trial site. For some trials, this meant frequent appointments to ensure appropriate data collection. Even the use of a 24-hour monitoring device required a patient to return to the trial site to allow for researchers access the stored data.
The rise of internet and Bluetooth-connected devices has allowed continuous monitoring and reporting of near real-time data. These data streams allow researchers and physicians to see patterns in biomarkers over time, develop near-real time interventions and identify patterns of use (or lack thereof) of drugs and devices.
These valuable data generation techniques have already created a huge shift in diabetes and cardiac research and care, as well as creating new approaches for the development of treatments across a variety of therapeutic areas. We are already seeing many trials where patient site visits are reducing. We are learning more from a variety of connected biometric monitoring devices, with many more still to be developed and accepted as useful in a research context, particularly as studies look at new biomarkers.
The science is evolving, and the current research using continuous monitoring is merely scratching the surface of what’s possible.
Steps towards standardization
While the ultimate goal is to use connected biometric monitoring devices to generate primary endpoints for trials, the results are more commonly used to support secondary or exploratory endpoints. For digital biomarkers to be accepted by the regulatory authorities as primary endpoints for clinical trials, the markers will need to be standardized and correlated with both existing biomarkers and clinical outcomes. Standardization is starting to happen for some outputs, such as those from heart rate monitors, where there is already a clear understanding of what the data should look like.
At the moment, each biometric monitoring device uses its own means for transmitting data in terms of frequency, format and structure. This hasn’t represented a significant concern until recently because each trial generally only used one or two sensors. As trials are increasingly dependent on multiple sensors for data collection, this is becoming a more sensitive area.
The standardization of basic parts of the data collection around structure and metadata will bring improvements in rapid use of the data rapidly and reduce the costs of cleaning data after collection. There have been some industry efforts to start standardization both across the industry (Institute of Electrical and Electronics Engineer – IEEE) and within therapeutic areas (Parkinson's disease – the Michael J Fox Foundation) but these are pretty far off.
There is significant resistance to standardization from the manufacturers of biometric monitoring devices. The concepts behind many of these devices are quite new and the manufacturers are concerned that standards will set up constraints on their capabilities long before they are well understood. The more general acceptance of digital biomarkers will take time, and while the standardization of data is a useful step, it's also important not to stifle innovation for some forms of data by laying down standards too early.
The massive growth in data provides some amazing opportunities for clinical research and could help biopharma organizations acquire better data in more affordable ways. Standardizing this data in order to tap into those opportunities has challenges, including getting the regulatory authorities to accept the approach, and protecting patients' health and privacy. It will take time before this data is being used in every trial or even every therapeutic area, however working with forward-thinking clinical researchers can put you on the leading edge to leverage this data to accelerate drug discovery and reduce costs.
You can find out more about this topic by going to IQVIA's digital health page, downloading the Growing Value of Digital Health Report, reading the Digital health: A new path to success, and following the IQVIA blog.
About the Author
Greg Plante brings 25 years of experience leading technology and business change. He has worked extensively across the life sciences life cycle, from drug discovery & device design through commercial & manufacturing operations. Greg currently leads Digital Health & Technology consulting for IQVIA. His experience in Digital Health began with the development of the one of the first Bluetooth enabled implantables and has continued through to the development of strategies and implementation of wearables for use in clinical trials today. Greg’s experiences prior to IQVIA include manufacturing consulting at Tunnell, leading Pharmaceutical R&D IT consulting for Deloitte, Pharma R&D Strategy for J&J and a variety of roles in CSC’s consulting organization.